Key result
A new predictive model incorporating respiratory pattern complexity indices (SampEn and LLE) alongside conventional metrics improved the prediction of weaning outcome compared to conventional metrics alone (AUC 0.916 vs 0.831).
Why the study?
Does respiratory pattern complexity analysis improve the prediction of weaning outcome compared to conventional indices in surgical critically ill patients?
Cohort (n=32)
Single-blind
No
Does respiratory pattern complexity analysis improve the prediction of weaning outcome compared to conventional indices in surgical critically ill patients?
Complexity analysis of respiratory signals, when combined with conventional indices, improves the prediction of weaning outcomes in surgical critically ill patients.
Complexity measures may refine weaning prediction beyond RSBI in surgical ICU patients; leaves open prospective validation before clinical adoption.
BACKGROUND: Separation from mechanical ventilation is a difficult task, whereas conventional predictive indices have not been proven accurate enough, so far. A few studies have explored changes of breathing pattern variability for weaning outcome prediction, with conflicting results. In this study, we tried to assess respiratory complexity during weaning trials, using different non-linear methods derived from theory of complex systems, in a cohort of surgical critically ill patients. RESULTS: Thirty two patients were enrolled in the study. There were 22 who passed and 10 who failed a weaning trial. Tidal volume and mean inspiratory flow were analyzed for 10 minutes during two phases: 1. pressure support (PS) ventilation (15-20 cm H2O) and 2. weaning trials with PS: 5 cm H2O. Sample entropy (SampEn), detrended fluctuation analysis (DFA) exponent, fractal dimension (FD) and largest lyapunov exponents (LLE) of the two respiratory parameters were computed in all patients and during the two phases of PS. Weaning failure patients exhibited significantly decreased respiratory pattern complexity, reflected in reduced sample entropy and lyapunov exponents and increased DFA exponents of respiratory flow time series, compared to weaning success subjects (p < 0.001). In addition, their changes were opposite between the two phases of the weaning trials. A new model including rapid shallow breathing index (RSBI), its product with airway occlusion pressure at 0.1 sec (P0.1), SampEn and LLE predicted better weaning outcome compared with RSBI, P0.1 and RSBI* P0.1 (conventional model, R(2) = 0.874 vs 0.643, p < 0.001). Areas under the curve were 0.916 vs 0.831, respectively (p < 0.05). CONCLUSIONS: We suggest that complexity analysis of respiratory signals can assess inherent breathing pattern dynamics and has increased prognostic impact upon weaning outcome in surgical patients.
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Papaioannou et al. (2011) conducted a cohort in Surgical critically ill patients undergoing weaning from mechanical ventilation (n=32). Multiparameter respiratory pattern complexity model (RSBI, RSBI*P0.1, SampEn, LLE) vs. Conventional model (RSBI, P0.1, RSBI*P0.1) was evaluated on Area under the ROC curve (AUC) for predicting weaning outcome (p=<0.05). A new predictive model incorporating respiratory pattern complexity indices (SampEn and LLE) alongside conventional metrics improved the prediction of weaning outcome compared to conventional metrics alone (AUC 0.916 vs 0.831).
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